Table REF  presents the results of our approach and comparisons with state-of-the-art (SOTA) methods, like deep variational metric learning (DVML) [1]}, energy confused adversarial metric learning (ECAML) [2]}, hierarchical triplet loss (HTL) [3]}, boosting independent embeddings robustly (BIER) [4]}, hard triplet generation (HTG) [5]}, attention-based ensemble (ALE) [6]}, as well as metric learning-based loss functions, like triplet with semi-hard mining [7]}, proxy NCA [8]}, N-pair [9]}, histogram [10]}, angular [11]}. To show that improvements carry over to R-50, we run EE [12]} with R-50 and compare the increase in (NMI, R@1, R@2, R@4) for LoOp vs. EE with triplet loss: (4.2, 6.0, 6.1 6.3) for GoogLeNet, (4.5, 6.1, 5.2, 4.1) for R-50. It can be seen that LoOp outperforms all the other methods for both clustering and retrieval tasks.
